Articles | Volume 15, issue 1
https://doi.org/10.5194/gi-15-53-2026
https://doi.org/10.5194/gi-15-53-2026
Research article
 | 
09 Feb 2026
Research article |  | 09 Feb 2026

Classification of sea-ice concentration from ship-board S-band radar images using open-source machine learning tools

Elizabeth Westbrook, Peter Gaube, Emmett Culhane, Frederick Bingham, Astrid Pacini, Carlyn Schmidgall, Julian Schanze, and Kyla Drushka

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Cited articles

Bridle, J. S.: Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition, in: Neurocomputing: Algorithms, architectures and applications, Springer, 227–236, https://doi.org/10.1007/978-3-642-76153-9_28, 1990. a, b
de Gélis, I., Colin, A., and Longépé, N.: Prediction of Categorized Sea Ice Concentration From Sentinel-1 SAR Images Based on a Fully Convolutional Network, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 5831–5841, https://doi.org/10.1109/JSTARS.2021.3074068, 2021. a
Drushka, K.: SASSIE Arctic Field Campaign Shipboard Thermos-salinograph Data Fall 2022, PO.DAAC [data set], https://doi.org/10.5067/SASSIE-TSG2, 2024. a
Drushka, K.: SASSIE Arctic Field Campaign Shipboard S-band Radar Data Fall 2022, PO.DAAC [data set], https://doi.org/10.5067/SASSIE-SBAND4, 2024b. 
Drushka, K., Westbrook, E., Bingham, F. M., Gaube, P., Dickinson, S., Fournier, S., Menezes, V., Misra, S., Pérez Valentín, J., Rainville, E. J., Schanze, J. J., Schmidgall, C., Shcherbina, A., Steele, M., Thomson, J., and Zippel, S.: Salinity and Stratification at the Sea Ice Edge (SASSIE): an oceanographic field campaign in the Beaufort Sea, Earth Syst. Sci. Data, 16, 4209–4242, https://doi.org/10.5194/essd-16-4209-2024, 2024. a, b, c, d
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Short summary
We develop a machine learning methods to detect and classify how much sea ice was present around our research vessel. We used a navigation radar common on many merchant vessels attached to a screen capture device. The captured images were classified using a convolutional neural network and the resulting classification were found to be in good agreement with direct observations and satellite-based products.
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